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Introduction Large language models such as OpenAI's (San Francisco, CA) ChatGPT-3.5 hold immense potential to augment self-directed learning in medicine, but concerns have risen regarding its accuracy in specialized fields. This study compares ChatGPT-3.5 with an internet search engine in their ability to define the Randleman criteria and its five parameters within a self-directed learning environment. Methods Twenty-three medical students gathered information on the Randleman criteria. Each student was allocated 10 minutes to interact with ChatGPT-3.5, followed by 10 minutes to search the internet independently. Each ChatGPT-3.5 conversation, student summary, and internet reference were subsequently analyzed for accuracy, efficiency, and reliability. Results ChatGPT-3.5 provided the correct definition for 26.1% of students (6/23, 95% CI: 12.3% to 46.8%), while an independent internet search resulted in sources containing the correct definition for 100% of students (23/23, 95% CI: 87.5% to 100%, p = 0.0001). ChatGPT-3.5 incorrectly identified the Randleman criteria as a corneal ectasia staging system for 17.4% of students (4/23), fabricated a "Randleman syndrome" for 4.3% of students (1/23), and gave no definition for 52.2% of students (12/23). When a definition was given (47.8%, 11/23), a median of two of the five correct parameters was provided along with a median of two additional falsified parameters. Conclusion Internet search engine outperformed ChatGPT-3.5 in providing accurate and reliable information on the Randleman criteria. ChatGPT-3.5 gave false information, required excessive prompting, and propagated misunderstandings. Learners should exercise discernment when using ChatGPT-3.5. Future initiatives should evaluate the implementation of prompt engineering and updated large-language models., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Hoopes Vision Ethics Board issued approval Exempt. The study was reviewed and received an exemption from the Hoopes Vision Ethics Board. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Tuttle et al.)

Purpose: Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease diagnosis and risk prediction using retinal images with good results., Methods: In this review, we systematically report studies with datasets of retinal images from patients with neurodegenerative diseases, including Alzheimer's disease, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, and others. We also review and characterize the models in the current literature which have been used for classification, regression, or segmentation problems using retinal images in patients with neurodegenerative diseases., Results: Our review found several existing datasets and models with various imaging modalities primarily in patients with Alzheimer's disease, with most datasets on the order of tens to a few hundred images. We found limited data available for the other neurodegenerative diseases. Although cross-sectional imaging data for Alzheimer's disease is becoming more abundant, datasets with longitudinal imaging of any disease are lacking., Conclusions: The use of bilateral and multimodal imaging together with metadata seems to improve model performance, thus multimodal bilateral image datasets with patient metadata are needed. We identified several deep learning tools that have been useful in this context including feature extraction algorithms specifically for retinal images, retinal image preprocessing techniques, transfer learning, feature fusion, and attention mapping. Importantly, we also consider the limitations common to these models in real-world clinical applications., Translational Relevance: This systematic review evaluates the deep learning models and retinal features relevant in the evaluation of retinal images of patients with neurodegenerative disease.

Background: Dentists bend needles prior to certain injections; however, there are concerns regarding needle fracture, lumen occlusion, and sharps handling. A previous study found that a 30-gauge needle fractures after four to nine 90° bends. This fatigue life study evaluated how many 90° bends a 30-gauge dental needle will sustain before fracture when bent using a needle guide., Methods: Two operators at Element Materials Technology, an independent testing, inspection, and certification company tested 48 30-gauge needles. After applying the needle guide, the operators bent the needle to a 90° angle and expressed the anesthetic from the tip. The needle was then bent back to a 0° angle, and the functionality was tested again. This process was repeated until the anesthetic failed to pass through the end of the needle due to fracture or obstruction. Each operator tested 24 needles (12 needles from each lot), and the number of sustained bends before the needle fracture was recorded., Results: The average number of sustained bends before needle failure was 40.33 (95% confidence interval = 37.41-43.26), with a minimum of 20, median of 40, and a maximum of 54. In each trial, the lumen remained patent until the needle fractured. The difference between the operators was statistically significant (P < 0.001). No significant differences in performance between needle lots were observed (P = 0.504)., Conclusion: Our results suggest that using a needle guide increases the number of sustained bends before needle fracture (P < 0.000001) than those reported in previous studies. Future studies should further evaluate the use of needle guides with other needle types across a variety of operators. Furthermore, additional opportunities lie in exploring workplace safety considerations and clinical applications of anesthetic delivery using a bent needle., (Copyright © 2023 Journal of Dental Anesthesia and Pain Medicine.)

Importance Chat Generative Pre-Trained Transformer (ChatGPT) has shown promising performance in various fields, including medicine, business, and law, but its accuracy in specialty-specific medical questions, particularly in ophthalmology, is still uncertain. Purpose This study evaluates the performance of two ChatGPT models (GPT-3.5 and GPT-4) and human professionals in answering ophthalmology questions from the StatPearls question bank, assessing their outcomes, and providing insights into the integration of artificial intelligence (AI) technology in ophthalmology. Methods ChatGPT's performance was evaluated using 467 ophthalmology questions from the StatPearls question bank. These questions were stratified into 11 subcategories, four difficulty levels, and three generalized anatomical categories. The answer accuracy of GPT-3.5, GPT-4, and human participants was assessed. Statistical analysis was conducted via the Kolmogorov-Smirnov test for normality, one-way analysis of variance (ANOVA) for the statistical significance of GPT-3 versus GPT-4 versus human performance, and repeated unpaired two-sample t-tests to compare the means of two groups. Results GPT-4 outperformed both GPT-3.5 and human professionals on ophthalmology StatPearls questions, except in the "Lens and Cataract" category. The performance differences were statistically significant overall, with GPT-4 achieving higher accuracy (73.2%) compared to GPT-3.5 (55.5%, p-value < 0.001) and humans (58.3%, p-value < 0.001). There were variations in performance across difficulty levels (rated one to four), but GPT-4 consistently performed better than both GPT-3.5 and humans on level-two, -three, and -four questions. On questions of level-four difficulty, human performance significantly exceeded that of GPT-3.5 (p = 0.008). Conclusion The study's findings demonstrate GPT-4's significant performance improvements over GPT-3.5 and human professionals on StatPearls ophthalmology questions. Our results highlight the potential of advanced conversational AI systems to be utilized as important tools in the education and practice of medicine., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Moshirfar et al.)